Xiangcheng Shi, Xiaoyun Lin, Ran Luo, Shican Wu, Lulu Li, Zhi-Jian Zhao* and Jinlong Gong*,
{"title":"Dynamics of Heterogeneous Catalytic Processes at Operando Conditions","authors":"Xiangcheng Shi, Xiaoyun Lin, Ran Luo, Shican Wu, Lulu Li, Zhi-Jian Zhao* and Jinlong Gong*, ","doi":"10.1021/jacsau.1c00355","DOIUrl":null,"url":null,"abstract":"<p >The rational design of high-performance catalysts is hindered by the lack of knowledge of the structures of active sites and the reaction pathways under reaction conditions, which can be ideally addressed by an <i>in situ</i>/<i>operando</i> characterization. Besides the experimental insights, a theoretical investigation that simulates reaction conditions─so-called <i>operando</i> modeling─is necessary for a plausible understanding of a working catalyst system at the atomic scale. However, there is still a huge gap between the current widely used computational model and the concept of <i>operando</i> modeling, which should be achieved through multiscale computational modeling. This Perspective describes various modeling approaches and machine learning techniques that step toward <i>operando</i> modeling, followed by selected experimental examples that present an <i>operando</i> understanding in the thermo- and electrocatalytic processes. At last, the remaining challenges in this area are outlined.</p>","PeriodicalId":94060,"journal":{"name":"JACS Au","volume":"1 12","pages":"2100–2120"},"PeriodicalIF":8.5000,"publicationDate":"2021-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/b5/60/au1c00355.PMC8715484.pdf","citationCount":"27","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JACS Au","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/jacsau.1c00355","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 27
Abstract
The rational design of high-performance catalysts is hindered by the lack of knowledge of the structures of active sites and the reaction pathways under reaction conditions, which can be ideally addressed by an in situ/operando characterization. Besides the experimental insights, a theoretical investigation that simulates reaction conditions─so-called operando modeling─is necessary for a plausible understanding of a working catalyst system at the atomic scale. However, there is still a huge gap between the current widely used computational model and the concept of operando modeling, which should be achieved through multiscale computational modeling. This Perspective describes various modeling approaches and machine learning techniques that step toward operando modeling, followed by selected experimental examples that present an operando understanding in the thermo- and electrocatalytic processes. At last, the remaining challenges in this area are outlined.